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START TIME: Tue Jul 2 15:43:11 UTC 2024
python3 version = Python 3.10.14
========================
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Already on 'bench_cluster'
M examples/config_tiny_llama.py
M examples/config_tiny_llama.yaml
M examples/train_tiny_llama.sh
M src/nanotron/models/llama.py
M src/nanotron/trainer.py
Your branch is up to date with 'origin/bench_cluster'.
Job status: RUNNING
W0702 15:43:15.500000 139963938871104 torch/distributed/run.py:757]
W0702 15:43:15.500000 139963938871104 torch/distributed/run.py:757] *****************************************
W0702 15:43:15.500000 139963938871104 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
W0702 15:43:15.500000 139963938871104 torch/distributed/run.py:757] *****************************************
W0702 15:43:19.727000 140037601847104 torch/distributed/run.py:757]
W0702 15:43:19.727000 140037601847104 torch/distributed/run.py:757] *****************************************
W0702 15:43:19.727000 140037601847104 torch/distributed/run.py:757] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
W0702 15:43:19.727000 140037601847104 torch/distributed/run.py:757] *****************************************
[default0]:07/02/2024 15:43:42 [WARNING|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Vocab Size Padding] Padded vocab (size: 50257) with 7 dummy tokens (new size: 50264)
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Config:
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Config(general=GeneralArgs(project='bench_cluster',
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: run='%date_%jobid',
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: seed=42,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: step=None,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: consumed_train_samples=None,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: benchmark_csv_path=None,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: ignore_sanity_checks=True),
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: parallelism=ParallelismArgs(dp=1,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pp=2,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tp=8,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7efee35fc730>,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tp_linear_async_communication=False,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: expert_parallel_size=1),
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: model=ModelArgs(model_config=LlamaConfig(bos_token_id=1,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: eos_token_id=2,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hidden_act='silu',
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hidden_size=2048,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: initializer_range=0.02,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: intermediate_size=4096,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: is_llama_config=True,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: max_position_embeddings=4096,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_attention_heads=32,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_hidden_layers=24,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_key_value_heads=32,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pad_token_id=None,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pretraining_tp=1,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rms_norm_eps=1e-05,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rope_scaling=None,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rope_theta=10000.0,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tie_word_embeddings=True,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: use_cache=True,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: vocab_size=50264),
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: init_method=RandomInit(std=0.025),
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: dtype=torch.bfloat16,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: make_vocab_size_divisible_by=1,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: ddp_bucket_cap_mb=25),
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2',
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tokenizer_revision=None,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tokenizer_max_length=None),
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'),
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: checkpoint_interval=100000,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: save_initial_state=False,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: resume_checkpoint_path=None,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: checkpoints_path_is_shared_file_system=False),
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: logging=LoggingArgs(log_level='info',
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: log_level_replica='info',
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: iteration_step_info_interval=1),
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tokens=TokensArgs(sequence_length=4096,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: train_steps=20,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: micro_batch_size=8,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: batch_accumulation_per_replica=128,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: val_check_interval=-1,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: limit_val_batches=0,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: limit_test_batches=0),
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: adam_beta1=0.9,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: adam_beta2=0.95,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: torch_adam_is_fused=True,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: name='adamW'),
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: zero_stage=1,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: weight_decay=0.01,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: clip_grad=1.0,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: accumulate_grad_in_fp32=True,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lr_warmup_steps=1,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lr_warmup_style='linear',
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lr_decay_style='linear',
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lr_decay_steps=19,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lr_decay_starting_step=None,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: min_decay_lr=1e-05)),
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: data_stages=[DatasetStageArgs(name='Training Stage',
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: start_training_step=1,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories',
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hf_dataset_splits='train',
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hf_dataset_config_name=None,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: dataset_processing_num_proc_per_process=64,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: dataset_overwrite_cache=False,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: text_column_name='text'),
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: seed=42,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_loading_workers=32))],
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-1_tp-8_pp-2_mbz-8')),
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: lighteval=None)
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Model Config:
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: LlamaConfig(bos_token_id=1,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: eos_token_id=2,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hidden_act='silu',
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: hidden_size=2048,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: initializer_range=0.02,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: intermediate_size=4096,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: is_llama_config=True,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: max_position_embeddings=4096,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_attention_heads=32,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_hidden_layers=24,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: num_key_value_heads=32,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pad_token_id=None,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: pretraining_tp=1,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rms_norm_eps=1e-05,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rope_scaling=None,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: rope_theta=10000.0,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: tie_word_embeddings=True,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: use_cache=True,
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: vocab_size=50264)
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Building model..
[default0]:07/02/2024 15:43:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Setting PP block ranks...
[default7]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=7|ip-26-0-171-56]: Local number of parameters: 65.3M (124.62MiB)
[default7]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=7|ip-26-0-171-56]: [After model building] Memory usage: 135.64MiB. Peak allocated: 137.67MiB Peak reserved: 150.00MiB
[default7]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=7|ip-26-0-171-56]: No checkpoint path provided.
[default0]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: Local number of parameters: 65.3M (124.62MiB)
[default0]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: [After model building] Memory usage: 135.64MiB. Peak allocated: 137.67MiB Peak reserved: 150.00MiB
[default0]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: No checkpoint path provided.
[default3]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=3|ip-26-0-171-56]: Local number of parameters: 65.3M (124.62MiB)
[default3]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=3|ip-26-0-171-56]: [After model building] Memory usage: 135.64MiB. Peak allocated: 137.67MiB Peak reserved: 150.00MiB
[default3]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=3|ip-26-0-171-56]: No checkpoint path provided.
[default5]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=5|ip-26-0-171-56]: Local number of parameters: 65.3M (124.62MiB)
[default5]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=5|ip-26-0-171-56]: [After model building] Memory usage: 135.64MiB. Peak allocated: 137.67MiB Peak reserved: 150.00MiB
[default5]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=5|ip-26-0-171-56]: No checkpoint path provided.
[default1]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=1|ip-26-0-171-56]: Local number of parameters: 65.3M (124.62MiB)
[default1]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=1|ip-26-0-171-56]: [After model building] Memory usage: 135.64MiB. Peak allocated: 137.67MiB Peak reserved: 150.00MiB
[default1]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=1|ip-26-0-171-56]: No checkpoint path provided.
[default6]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=6|ip-26-0-171-56]: Local number of parameters: 65.3M (124.62MiB)
[default6]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=6|ip-26-0-171-56]: [After model building] Memory usage: 135.64MiB. Peak allocated: 137.67MiB Peak reserved: 150.00MiB
[default6]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=6|ip-26-0-171-56]: No checkpoint path provided.
[default2]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=2|ip-26-0-171-56]: Local number of parameters: 65.3M (124.62MiB)
[default2]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=2|ip-26-0-171-56]: [After model building] Memory usage: 135.64MiB. Peak allocated: 137.67MiB Peak reserved: 150.00MiB
[default2]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=2|ip-26-0-171-56]: No checkpoint path provided.
[default4]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=4|ip-26-0-171-56]: Local number of parameters: 65.3M (124.62MiB)
[default4]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=4|ip-26-0-171-56]: [After model building] Memory usage: 135.64MiB. Peak allocated: 137.67MiB Peak reserved: 150.00MiB
[default4]:07/02/2024 15:43:59 [INFO|DP=0|PP=1|TP=4|ip-26-0-171-56]: No checkpoint path provided.
[default7]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=7|ip-26-0-160-225]: Local number of parameters: 86.3M (164.65MiB)
[default7]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=7|ip-26-0-160-225]: [After model building] Memory usage: 179.67MiB. Peak allocated: 181.70MiB Peak reserved: 198.00MiB
[default7]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=7|ip-26-0-160-225]: No checkpoint path provided.
[default5]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=5|ip-26-0-160-225]: Local number of parameters: 86.3M (164.65MiB)
[default5]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=5|ip-26-0-160-225]: [After model building] Memory usage: 179.67MiB. Peak allocated: 181.70MiB Peak reserved: 198.00MiB
[default0]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Total number of parameters: 1.21G (2314.22MiB)
[default0]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Local number of parameters: 86.3M (164.65MiB)
[default0]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [After model building] Memory usage: 179.67MiB. Peak allocated: 181.70MiB Peak reserved: 198.00MiB
[default0]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: No checkpoint path provided.
[default0]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Parametrizing model parameters using StandardParametrizator
[default5]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=5|ip-26-0-160-225]: No checkpoint path provided.
[default4]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=4|ip-26-0-160-225]: Local number of parameters: 86.3M (164.65MiB)
[default4]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=4|ip-26-0-160-225]: [After model building] Memory usage: 179.67MiB. Peak allocated: 181.70MiB Peak reserved: 198.00MiB
[default4]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=4|ip-26-0-160-225]: No checkpoint path provided.
[default2]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-225]: Local number of parameters: 86.3M (164.65MiB)
[default2]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-225]: [After model building] Memory usage: 179.67MiB. Peak allocated: 181.70MiB Peak reserved: 198.00MiB
[default2]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=2|ip-26-0-160-225]: No checkpoint path provided.
[default6]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=6|ip-26-0-160-225]: Local number of parameters: 86.3M (164.65MiB)
[default6]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=6|ip-26-0-160-225]: [After model building] Memory usage: 179.67MiB. Peak allocated: 181.70MiB Peak reserved: 198.00MiB
[default6]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=6|ip-26-0-160-225]: No checkpoint path provided.
[default1]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-225]: Local number of parameters: 86.3M (164.65MiB)
[default1]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-225]: [After model building] Memory usage: 179.67MiB. Peak allocated: 181.70MiB Peak reserved: 198.00MiB
[default1]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=1|ip-26-0-160-225]: No checkpoint path provided.
[default3]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-225]: Local number of parameters: 86.3M (164.65MiB)
[default3]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-225]: [After model building] Memory usage: 179.67MiB. Peak allocated: 181.70MiB Peak reserved: 198.00MiB
[default3]:07/02/2024 15:43:59 [INFO|DP=0|PP=0|TP=3|ip-26-0-160-225]: No checkpoint path provided.
[default0]:07/02/2024 15:44:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Optimizer Building] Using LearningRateForSP as learning rate
[default0]:07/02/2024 15:44:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] Size of optimizer params per rank:
[default0]:07/02/2024 15:44:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [ZeRO sharding] DP Rank 0 has 86.3M out of 86.3M (100.00%) params' optimizer states
[default0]:07/02/2024 15:44:01 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples
[default0]:07/02/2024 15:44:01 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Using `datasets` library
[default0]:07/02/2024 15:44:01 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4')
[default0]:07/02/2024 15:44:02 [WARNING|DP=0|PP=0|TP=0|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 15:44:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Training Plan] There are 1 training stages
[default0]:07/02/2024 15:44:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Stage Training Stage] start from step 1
[default0]:07/02/2024 15:44:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]:
[default0]:07/02/2024 15:44:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: [Start training] datetime: 2024-07-02 15:44:04.143046 | mbs: 8 | grad_accum: 128 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0
[default0]:07/02/2024 15:44:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps
[default0]:07/02/2024 15:44:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 839.67MiB. Peak allocated 839.67MiB. Peak reserved: 858.00MiB
[default7]:07/02/2024 15:44:04 [WARNING|DP=0|PP=1|TP=7|ip-26-0-171-56]: Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 15:44:04 [WARNING|DP=0|PP=1|TP=3|ip-26-0-171-56]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/02/2024 15:44:04 [WARNING|DP=0|PP=1|TP=5|ip-26-0-171-56]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/02/2024 15:44:04 [WARNING|DP=0|PP=1|TP=4|ip-26-0-171-56]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/02/2024 15:44:04 [WARNING|DP=0|PP=1|TP=2|ip-26-0-171-56]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 15:44:04 [WARNING|DP=0|PP=1|TP=6|ip-26-0-171-56]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:07/02/2024 15:44:04 [WARNING|DP=0|PP=0|TP=7|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:07/02/2024 15:44:04 [WARNING|DP=0|PP=0|TP=4|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default2]:07/02/2024 15:44:04 [WARNING|DP=0|PP=0|TP=2|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default4]:Repo card metadata block was not found. Setting CardData to empty.
[default7]:Repo card metadata block was not found. Setting CardData to empty.
[default2]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:Repo card metadata block was not found. Setting CardData to empty.
[default3]:07/02/2024 15:44:04 [WARNING|DP=0|PP=0|TP=3|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default0]:Repo card metadata block was not found. Setting CardData to empty.
[default0]:07/02/2024 15:44:04 [WARNING|DP=0|PP=1|TP=0|ip-26-0-171-56]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/02/2024 15:44:04 [WARNING|DP=0|PP=1|TP=1|ip-26-0-171-56]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default5]:07/02/2024 15:44:04 [WARNING|DP=0|PP=0|TP=5|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default5]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:07/02/2024 15:44:04 [WARNING|DP=0|PP=0|TP=6|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:07/02/2024 15:44:04 [WARNING|DP=0|PP=0|TP=1|ip-26-0-160-225]: Repo card metadata block was not found. Setting CardData to empty.
[default1]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:Repo card metadata block was not found. Setting CardData to empty.
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default2]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default7]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default4]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default5]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default2]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default7]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default0]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default1]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default6]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
[default3]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]: warnings.warn(
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default2]: warnings.warn(
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]: warnings.warn(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default6]: warnings.warn(
[default4]: warnings.warn(
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default3]: warnings.warn(
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]: warnings.warn(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]: warnings.warn(
[default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default4]: warnings.warn(
[default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default7]: warnings.warn(
[default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]: warnings.warn(
[default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default3]: warnings.warn(
[default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default1]: warnings.warn(
[default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default0]:07/02/2024 15:44:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 910.95MiB. Peak allocated 15888.46MiB. Peak reserved: 16202.00MiB
[default6]: warnings.warn(
[default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default5]: warnings.warn(
[default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
[default2]: warnings.warn(
[default0]:07/02/2024 15:44:48 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: iteration: 1 / 20 | consumed_tokens: 4.19M | elapsed_time_per_iteration_ms: 42K | tokens_per_sec: 99.9K | tokens_per_sec_per_gpu: 6.24K | global_batch_size: 1.02K | lm_loss: 11.2 | lr: 0.0001 | model_tflops_per_gpu: 56.6 | hardware_tflops_per_gpu: 56.6 | grad_norm: 12.1 | cuda_memory_allocated: 1.26G | cuda_max_memory_reserved: 9.35G | hd_total_memory_tb: 312G | hd_used_memory_tb: 65.6G | hd_free_memory_tb: 247G
[default0]:07/02/2024 15:44:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 1572.43MiB. Peak reserved: 16202.00MiB
[default0]:07/02/2024 15:45:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 16549.94MiB. Peak reserved: 16842.00MiB
[default0]:07/02/2024 15:45:07 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: iteration: 2 / 20 | consumed_tokens: 8.39M | elapsed_time_per_iteration_ms: 18.5K | tokens_per_sec: 227K | tokens_per_sec_per_gpu: 14.2K | global_batch_size: 1.02K | lm_loss: 11.2 | lr: 9.53e-05 | model_tflops_per_gpu: 129 | hardware_tflops_per_gpu: 129 | grad_norm: 12.2 | cuda_memory_allocated: 1.26G | cuda_max_memory_reserved: 9.35G | hd_total_memory_tb: 312G | hd_used_memory_tb: 65.6G | hd_free_memory_tb: 247G
[default0]:07/02/2024 15:45:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 1572.47MiB. Peak reserved: 16842.00MiB
[default0]:07/02/2024 15:45:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 16549.94MiB. Peak reserved: 16842.00MiB
[default0]:STAGE:2024-07-02 15:45:25 1190124:1190124 ActivityProfilerController.cpp:314] Completed Stage: Warm Up
[default0]:07/02/2024 15:45:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 1572.47MiB. Peak reserved: 16842.00MiB
[default0]:07/02/2024 15:45:25 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: iteration: 3 / 20 | consumed_tokens: 12.6M | elapsed_time_per_iteration_ms: 18.6K | tokens_per_sec: 225K | tokens_per_sec_per_gpu: 14.1K | global_batch_size: 1.02K | lm_loss: 10 | lr: 9.05e-05 | model_tflops_per_gpu: 128 | hardware_tflops_per_gpu: 128 | grad_norm: 51.7 | cuda_memory_allocated: 1.26G | cuda_max_memory_reserved: 9.35G | hd_total_memory_tb: 312G | hd_used_memory_tb: 65.6G | hd_free_memory_tb: 247G
[default0]:07/02/2024 15:45:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 16549.94MiB. Peak reserved: 16842.00MiB
[default0]:07/02/2024 15:45:46 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: iteration: 4 / 20 | consumed_tokens: 16.8M | elapsed_time_per_iteration_ms: 20.4K | tokens_per_sec: 206K | tokens_per_sec_per_gpu: 12.9K | global_batch_size: 1.02K | lm_loss: 11.7 | lr: 8.58e-05 | model_tflops_per_gpu: 117 | hardware_tflops_per_gpu: 117 | grad_norm: 18.3 | cuda_memory_allocated: 1.26G | cuda_max_memory_reserved: 9.35G | hd_total_memory_tb: 312G | hd_used_memory_tb: 65.6G | hd_free_memory_tb: 247G
[default0]:07/02/2024 15:45:46 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 1572.47MiB. Peak reserved: 16842.00MiB
[default0]:07/02/2024 15:46:06 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: iteration: 5 / 20 | consumed_tokens: 21M | elapsed_time_per_iteration_ms: 20.4K | tokens_per_sec: 205K | tokens_per_sec_per_gpu: 12.8K | global_batch_size: 1.02K | lm_loss: 10.4 | lr: 8.11e-05 | model_tflops_per_gpu: 116 | hardware_tflops_per_gpu: 116 | grad_norm: 16
[default0]:07/02/2024 15:46:06 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 16549.94MiB. Peak reserved: 16842.00MiB
[default0]:07/02/2024 15:46:27 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: iteration: 6 / 20 | consumed_tokens: 25.2M | elapsed_time_per_iteration_ms: 20.4K | tokens_per_sec: 205K | tokens_per_sec_per_gpu: 12.8K | global_batch_size: 1.02K | lm_loss: 9.9 | lr: 7.63e-05 | model_tflops_per_gpu: 117 | hardware_tflops_per_gpu: 117 | grad_norm: 9.06
[default0]:STAGE:2024-07-02 15:47:09 1190124:1190124 ActivityProfilerController.cpp:320] Completed Stage: Collection
[default0]:STAGE:2024-07-02 15:47:14 1190124:1190124 ActivityProfilerController.cpp:324] Completed Stage: Post Processing
[default0]:07/02/2024 15:52:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 16549.94MiB. Peak reserved: 16842.00MiB
[default0]:07/02/2024 15:53:06 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 16549.94MiB. Peak reserved: 16842.00MiB
[default0]:07/02/2024 15:53:06 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: iteration: 7 / 20 | consumed_tokens: 29.4M | elapsed_time_per_iteration_ms: 399K | tokens_per_sec: 10.5K | tokens_per_sec_per_gpu: 657 | global_batch_size: 1.02K | lm_loss: 9.37 | lr: 7.16e-05 | model_tflops_per_gpu: 5.96 | hardware_tflops_per_gpu: 5.96 | grad_norm: 6.23
[default0]:07/02/2024 15:53:24 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 16549.94MiB. Peak reserved: 16842.00MiB
[default0]:07/02/2024 15:53:24 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: iteration: 8 / 20 | consumed_tokens: 33.6M | elapsed_time_per_iteration_ms: 18.3K | tokens_per_sec: 229K | tokens_per_sec_per_gpu: 14.3K | global_batch_size: 1.02K | lm_loss: 8.89 | lr: 6.68e-05 | model_tflops_per_gpu: 130 | hardware_tflops_per_gpu: 130 | grad_norm: 5.76
[default0]:07/02/2024 15:53:42 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: iteration: 9 / 20 | consumed_tokens: 37.7M | elapsed_time_per_iteration_ms: 18.2K | tokens_per_sec: 230K | tokens_per_sec_per_gpu: 14.4K | global_batch_size: 1.02K | lm_loss: 8.81 | lr: 6.21e-05 | model_tflops_per_gpu: 131 | hardware_tflops_per_gpu: 131 | grad_norm: 11.2
[default0]:07/02/2024 15:53:42 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 16549.94MiB. Peak reserved: 16842.00MiB
[default0]:07/02/2024 15:54:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 16549.94MiB. Peak reserved: 16842.00MiB
[default0]:07/02/2024 15:54:00 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: iteration: 10 / 20 | consumed_tokens: 41.9M | elapsed_time_per_iteration_ms: 17.9K | tokens_per_sec: 234K | tokens_per_sec_per_gpu: 14.6K | global_batch_size: 1.02K | lm_loss: 8.34 | lr: 5.74e-05 | model_tflops_per_gpu: 133 | hardware_tflops_per_gpu: 133 | grad_norm: 5.72
[default0]:07/02/2024 15:54:18 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 16549.94MiB. Peak reserved: 16842.00MiB
[default0]:07/02/2024 15:54:18 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: iteration: 11 / 20 | consumed_tokens: 46.1M | elapsed_time_per_iteration_ms: 18K | tokens_per_sec: 233K | tokens_per_sec_per_gpu: 14.6K | global_batch_size: 1.02K | lm_loss: 8.06 | lr: 5.26e-05 | model_tflops_per_gpu: 132 | hardware_tflops_per_gpu: 132 | grad_norm: 4.91
[default0]:07/02/2024 15:54:36 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 16549.94MiB. Peak reserved: 16842.00MiB
[default0]:07/02/2024 15:54:36 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: iteration: 12 / 20 | consumed_tokens: 50.3M | elapsed_time_per_iteration_ms: 18.2K | tokens_per_sec: 231K | tokens_per_sec_per_gpu: 14.4K | global_batch_size: 1.02K | lm_loss: 7.9 | lr: 4.79e-05 | model_tflops_per_gpu: 131 | hardware_tflops_per_gpu: 131 | grad_norm: 4.86
[default0]:07/02/2024 15:54:54 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 16549.94MiB. Peak reserved: 16842.00MiB
[default0]:07/02/2024 15:54:54 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: iteration: 13 / 20 | consumed_tokens: 54.5M | elapsed_time_per_iteration_ms: 17.9K | tokens_per_sec: 234K | tokens_per_sec_per_gpu: 14.6K | global_batch_size: 1.02K | lm_loss: 7.75 | lr: 4.32e-05 | model_tflops_per_gpu: 133 | hardware_tflops_per_gpu: 133 | grad_norm: 4.69
[default0]:07/02/2024 15:55:13 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 16549.94MiB. Peak reserved: 16842.00MiB
[default0]:07/02/2024 15:55:13 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: iteration: 14 / 20 | consumed_tokens: 58.7M | elapsed_time_per_iteration_ms: 18.8K | tokens_per_sec: 223K | tokens_per_sec_per_gpu: 13.9K | global_batch_size: 1.02K | lm_loss: 7.62 | lr: 3.84e-05 | model_tflops_per_gpu: 126 | hardware_tflops_per_gpu: 126 | grad_norm: 4.69
[default0]:07/02/2024 15:55:31 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 16549.94MiB. Peak reserved: 16842.00MiB
[default0]:07/02/2024 15:55:31 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: iteration: 15 / 20 | consumed_tokens: 62.9M | elapsed_time_per_iteration_ms: 18.2K | tokens_per_sec: 231K | tokens_per_sec_per_gpu: 14.4K | global_batch_size: 1.02K | lm_loss: 7.48 | lr: 3.37e-05 | model_tflops_per_gpu: 131 | hardware_tflops_per_gpu: 131 | grad_norm: 4.49
[default0]:07/02/2024 15:55:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 16549.94MiB. Peak reserved: 16842.00MiB
[default0]:07/02/2024 15:55:49 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: iteration: 16 / 20 | consumed_tokens: 67.1M | elapsed_time_per_iteration_ms: 17.9K | tokens_per_sec: 234K | tokens_per_sec_per_gpu: 14.6K | global_batch_size: 1.02K | lm_loss: 7.35 | lr: 2.89e-05 | model_tflops_per_gpu: 133 | hardware_tflops_per_gpu: 133 | grad_norm: 3.99
[default0]:07/02/2024 15:56:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 16549.94MiB. Peak reserved: 16842.00MiB
[default0]:07/02/2024 15:56:07 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: iteration: 17 / 20 | consumed_tokens: 71.3M | elapsed_time_per_iteration_ms: 18K | tokens_per_sec: 233K | tokens_per_sec_per_gpu: 14.5K | global_batch_size: 1.02K | lm_loss: 7.23 | lr: 2.42e-05 | model_tflops_per_gpu: 132 | hardware_tflops_per_gpu: 132 | grad_norm: 3.54
[default0]:07/02/2024 15:56:25 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: iteration: 18 / 20 | consumed_tokens: 75.5M | elapsed_time_per_iteration_ms: 18K | tokens_per_sec: 234K | tokens_per_sec_per_gpu: 14.6K | global_batch_size: 1.02K | lm_loss: 7.16 | lr: 1.95e-05 | model_tflops_per_gpu: 133 | hardware_tflops_per_gpu: 133 | grad_norm: 3.28
[default0]:07/02/2024 15:56:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 16549.94MiB. Peak reserved: 16842.00MiB
[default0]:07/02/2024 15:56:43 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: iteration: 19 / 20 | consumed_tokens: 79.7M | elapsed_time_per_iteration_ms: 18.2K | tokens_per_sec: 231K | tokens_per_sec_per_gpu: 14.4K | global_batch_size: 1.02K | lm_loss: 7.09 | lr: 1.47e-05 | model_tflops_per_gpu: 131 | hardware_tflops_per_gpu: 131 | grad_norm: 3.2
[default0]:07/02/2024 15:56:43 [INFO|DP=0|PP=0|TP=0|ip-26-0-160-225]: Memory usage: 1572.43MiB. Peak allocated 16549.94MiB. Peak reserved: 16842.00MiB
[default0]:07/02/2024 15:57:02 [INFO|DP=0|PP=1|TP=0|ip-26-0-171-56]: iteration: 20 / 20 | consumed_tokens: 83.9M | elapsed_time_per_iteration_ms: 18.7K | tokens_per_sec: 224K | tokens_per_sec_per_gpu: 14K | global_batch_size: 1.02K | lm_loss: 7.03 | lr: 1e-05 | model_tflops_per_gpu: 127 | hardware_tflops_per_gpu: 127 | grad_norm: 3.1
Saved 1 csv files over 1 completed logs
Processing file: /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-1_tp-8_pp-2_mbz-8/profiler/ip-26-0-160-225_1190124.1719935492562546823.pt.trace.json
Results written to /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-1_tp-8_pp-2_mbz-8/profiler.csv
Consider using `hf_transfer` for faster uploads. This solution comes with some limitations. See https://huggingface.co/docs/huggingface_hub/hf_transfer for more details.
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